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Exposure to continuous moderate noise levels is known to impair the auditory system leading to Noise-Induced Hearing Loss (NIHL) in animals including humans. The mechanism underlying noise-dependent auditory Temporary Threshold Shifts (TTS) is not fully understood. In fact, only limited information is available on vertebrates such as fishes, which share homologous inner ear structures to mammals and have the ability to regenerate hair cells. The zebrafish Danio rerio is a well-established model in hearing research providing an unmatched opportunity to investigate the molecular and physiological mechanisms of NIHL at the sensory receptor level. Here we investigated for the first time the effects of noise exposure on TTS and functional recovery in zebrafish, as well as the associated morphological damage and regeneration of the inner ear saccular hair cells. Adult specimens were exposed for 24h to white noise at various amplitudes (130, 140 and 150 dB re. 1 μPa) and their auditory sensitivity was subsequently measured with the Auditory Evoked Potential (AEP) recording technique. Sensory recovery was tested at different times post-treatment (after 3, 7 and 14 days) and compared to individuals kept under quiet lab conditions. Results revealed noise level-dependent TTS up to 33 dB and increase in response latency. Recovery of hearing function occurred within 7 days for fish exposed to 130 and 140 dB noise levels, while fish subject to 150 dB only returned to baseline thresholds after 14 days. Hearing impairment was accompanied by significant loss of hair cells only at the highest noise treatment. Full regeneration of the sensory tissue (number of hair cell receptors) occurred within 7 days, which was prior to functional recovery. We provide first baseline data of NIHL in zebrafish and validate this species as an effective vertebrate model to investigate the impact of noise exposure on the structure and function of the adult inner ear and its recovery process.
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The doctrine of original sin gives people the impression that the goodness of human nature is under-evaluated in the Christian theological tradition. The Chinese philosopher Mencius is famous for his teaching on the goodness of human nature. Reading Mencius and Thomas Aquinas side by side, this article argues that the Mencian teaching on human nature brings us to affirm the goodness of human nature by recovering the significance of the image of God for the Christian doctrine of human nature. If we seek the goodness of human nature in the possibilities to become good, it is natural to see that even in the fallen state the possibilities of becoming like God remain in human nature imprinted with the image of God. It is open to the culmination of a gradual progression to its perfection.
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Design for Classroom Units: A Collaborative Multicultural Studio Development with Chinese Students
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The design thinking methodology is a problem-solving approach that involves empathising with end-users, (re)defining problems, brainstorming solutions creatively, and experimenting with prototypes and testing. It has been widely adopted in education to help students develop critical thinking, creativity, and problem-solving skills in design. On the other hand, text-to-image artificial intelligence is a method used to generate images from natural language descriptors (usually referred to as prompts). Design thinking methodology can teach students to think creatively and critically about real-world problems when applied in the classroom. In the context of design teaching at the University of Saint Joseph, Macao, students use the design thinking methodology to develop innovative proposals for furniture design solutions. Combining design thinking methodologies with text-to-image artificial intelligence can further enhance the learning experience by allowing students to generate visual representations of their ideas during the ideation phase. The authors developed a systematic approach to generate images for ideation on furniture design based on prompting text-to-image (PTI). The analysis related students’ results who applied the design thinking methodology without using AI tools and the results generated using a standard text-to-image programme. By combining both methods, teachers can help students develop critical thinking, creativity, and problem-solving skills, while also allowing them to generate visual representations in a different paradigm and, by so, being able to communicate their ideas with the most appropriate support for them.
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Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.
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